import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__ )      ->str:




  '''simple docstring'''

  _UpperCamelCase           =    StableDiffusionPipeline.from_pretrained(a__    ,     torch_dtype=torch.floataa )

  # load LoRA weight from .safetensors
  _UpperCamelCase           =    load_file(a__ )

  _UpperCamelCase           =    []

  # directly update weight in diffusers model
  for key in state_dict:
    # it is suggested to print out the key, it usually will be something like below
    # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

    # as we have set the alpha beforehand, so just skip
    if ".alpha" in key or key in visited:
      continue

    if "text" in key:
      _UpperCamelCase           =    key.split("." )[0].split(LORA_PREFIX_TEXT_ENCODER + "_" )[-1].split("_" )
      _UpperCamelCase           =    pipeline.text_encoder
    else:
      _UpperCamelCase           =    key.split("." )[0].split(LORA_PREFIX_UNET + "_" )[-1].split("_" )
      _UpperCamelCase           =    pipeline.unet

    # find the target layer
    _UpperCamelCase           =    layer_infos.pop(0 )
    while len(a__ ) > -1:
      try:
        _UpperCamelCase           =    curr_layer.__getattr__(a__ )
        if len(a__ ) > 0:
          _UpperCamelCase           =    layer_infos.pop(0 )
        elif len(a__ ) == 0:
          break
      except Exception:
        if len(a__ ) > 0:
          temp_name += "_" + layer_infos.pop(0 )
        else:
          _UpperCamelCase           =    layer_infos.pop(0 )

    _UpperCamelCase           =    []
    if "lora_down" in key:
      pair_keys.append(key.replace("lora_down"    ,     "lora_up" ) )
      pair_keys.append(a__ )
    else:
      pair_keys.append(a__ )
      pair_keys.append(key.replace("lora_up"    ,     "lora_down" ) )

    # update weight
    if len(state_dict[pair_keys[0]].shape ) == 4:
      _UpperCamelCase           =    state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
      _UpperCamelCase           =    state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
      curr_layer.weight.data += alpha * torch.mm(a__    ,     a__ ).unsqueeze(2 ).unsqueeze(3 )
    else:
      _UpperCamelCase           =    state_dict[pair_keys[0]].to(torch.floataa )
      _UpperCamelCase           =    state_dict[pair_keys[1]].to(torch.floataa )
      curr_layer.weight.data += alpha * torch.mm(a__    ,     a__ )

    # update visited list
    for item in pair_keys:
      visited.append(a__ )

  return pipeline


if __name__ == "__main__":
   lowerCamelCase__  =					argparse.ArgumentParser()

   parser.add_argument(
       '''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
   )
   parser.add_argument(
       '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
   )
   parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
   parser.add_argument(
       '''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
   )
   parser.add_argument(
       '''--lora_prefix_text_encoder''',
       default='''lora_te''',
       type=str,
       help='''The prefix of text encoder weight in safetensors''',
   )
   parser.add_argument('''--alpha''', default=0.75, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
   parser.add_argument(
       '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
   )
   parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')

   lowerCamelCase__  =					parser.parse_args()

   lowerCamelCase__  =					args.base_model_path
   lowerCamelCase__  =					args.checkpoint_path
   lowerCamelCase__  =					args.dump_path
   lowerCamelCase__  =					args.lora_prefix_unet
   lowerCamelCase__  =					args.lora_prefix_text_encoder
   lowerCamelCase__  =					args.alpha

   lowerCamelCase__  =					convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

   lowerCamelCase__  =					pipe.to(args.device)
   pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowerCamelCase__  =					get_logger(__name__)




class 					_UpperCAmelCase			(     enum.Enum  ):



 '''simple docstring'''




 __A			       =					'''all_checks'''
 __A			       =					'''basic_checks'''
 __A			       =					'''no_checks'''




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''






def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__=None )      ->int:




  '''simple docstring'''

  if expected_checksums is None:
    logger.info("Unable to verify checksums." )
    return
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(a__ ) - set(a__ ) ) )
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise UnexpectedDownloadedFile(str(set(a__ ) - set(a__ ) ) )
  _UpperCamelCase           =    [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  _UpperCamelCase           =    " for " + verification_name if verification_name is not None else ""
  if len(a__ ) > 0:
    raise NonMatchingChecksumError(
        f'Checksums didn\'t match{for_verification_name}:\n'
        f'{bad_urls}\n'
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
  logger.info("All the checksums matched successfully" + for_verification_name )




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''






def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Dict:




  '''simple docstring'''

  if expected_splits is None:
    logger.info("Unable to verify splits sizes." )
    return
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise ExpectedMoreSplits(str(set(a__ ) - set(a__ ) ) )
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise UnexpectedSplits(str(set(a__ ) - set(a__ ) ) )
  _UpperCamelCase           =    [
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(a__ ) > 0:
    raise NonMatchingSplitsSizesError(str(a__ ) )
  logger.info("All the splits matched successfully." )


def 				lowerCAmelCase__       (			a__    ,     a__ = True )      ->dict:




  '''simple docstring'''

  if record_checksum:
    _UpperCamelCase           =    shaaaa()
    with open(a__    ,     "rb" ) as f:
      for chunk in iter(lambda: f.read(1 << 20 )    ,     B"" ):
        m.update(a__ )
      _UpperCamelCase           =    m.hexdigest()
  else:
    _UpperCamelCase           =    None
  return {"num_bytes": os.path.getsize(a__ ), "checksum": checksum}







def 				lowerCAmelCase__       (			a__ )      ->Any:




  '''simple docstring'''

  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False




